trabajo_final_assereto_mora
Mora Assereto Farroni
2022-08-17
Trabajo Final Seguridad Vial
Alumna: Mora Assereto Farroni
Objetivo: El objetivo del trabajo es encontrar características urbanas para determinar la vulnearbilidad de los usuarios viales. De este modo se busca evaluar qué políticas públicas son más eficacespara alcanzar nivel de fatalidad y accidentes de tránsito violentos cero
Entendimiento de datos
El modelo se basa en una serie de variables Variables independientes son:
DenPob: densidad poblacional (población/km2)ArCiclista: relación entre el área total para ciclistas y el área total para conducir.ArBajaVel: relación entre el área total limitada para baja velocidad y el área total para conducir.PMPeatones: participación modal para caminar(%).PMCiclistas: participación modal para cyclistas (%).PMTPublico: participación modal para transporte público (%).PMVMotor: participación modal para vehículos con motor (%)Temp: temperatura media anual (C).Precipitacion: precipitación media anual (mm).PBI: producto bruto interno medio per cápita (EUR).
Por otra parte, como variables de respuesta tenemos el índice MDS que provocan choques del vehículo A al B (A → B), lo notamos AB.
PeatAuto: Peaton → Auto o Taxi.CicAuto: Ciclista → Auto o Taxi.V2RMSM:Vehículo con dos ruedas a motor → si mismos.V2RMAutoVehículo con dos ruedas a motor → Auto o Taxi.AutoSMAuto o Taxi → si mismos.AutoAutoAuto o Taxi → Auto o Taxi.
Librerias
library(dplyr)##
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## loadingslibrary(readxl)Recoleción de datos
segvial <- read_excel("SegVial.xlsx")
segvial## # A tibble: 24 × 20
## `Codigo Pais` Pais Ciudad Poblacion DenPob ArCiclista ArBajaVel PMPeatones
## <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 ES Spain Barcel… 1620343 16014 0.15 0.775 32.0
## 2 ES Spain Madrid 3223334 5332 0.067 0.065 34.0
## 3 FR France Bordea… 254436 5104 0.106 0.239 21.0
## 4 FR France Lille 232787 6687 0.098 0.634 32.0
## 5 FR France Lyon 516092 10758 0.109 0.284 34.0
## 6 FR France Marsei… 863310 3564 0.044 0.118 34.0
## 7 FR France Montpe… 285121 4997 0.167 0.125 26.0
## 8 FR France Nantes 309346 4704 0.327 0.354 27.0
## 9 FR France Nice 340017 4586 0.056 0.102 40.0
## 10 FR France Paris 2187526 20772 0.186 0.491 47.0
## # … with 14 more rows, and 12 more variables: PMCiclistas <chr>,
## # PMTPublico <chr>, PMVMotor <chr>, PeatAuto <dbl>, CicAuto <dbl>,
## # V2RMSM <dbl>, V2RMAuto <dbl>, AutoSM <dbl>, AutoAuto <dbl>,
## # Precipitacion <chr>, Temp <chr>, PBI <dbl>attach(segvial)Explorar los datos
glimpse(segvial)## Rows: 24
## Columns: 20
## $ `Codigo Pais` <chr> "ES", "ES", "FR", "FR", "FR", "FR", "FR", "FR", "FR", "F…
## $ Pais <chr> "Spain", "Spain", "France", "France", "France", "France"…
## $ Ciudad <chr> "Barcelona", "Madrid", "Bordeaux", "Lille", "Lyon", "Mar…
## $ Poblacion <dbl> 1620343, 3223334, 254436, 232787, 516092, 863310, 285121…
## $ DenPob <dbl> 16014, 5332, 5104, 6687, 10758, 3564, 4997, 4704, 4586, …
## $ ArCiclista <chr> "0.15", "0.067", "0.106", "0.098", "0.109", "0.044", "0.…
## $ ArBajaVel <chr> "0.775", "0.065", "0.239", "0.634", "0.284", "0.118", "0…
## $ PMPeatones <chr> "32.0", "34.0", "21.0", "32.0", "34.0", "34.0", "26.0", …
## $ PMCiclistas <chr> "2.0", "0.46", "3.0", "2.0", "2.0", "1.0", "2.0", "5.0",…
## $ PMTPublico <chr> "39.0", "24.35", "9.0", "10.0", "19.0", "11.0", "8.0", "…
## $ PMVMotor <chr> "27.0", "39.89", "67.0", "56.0", "45.0", "54.0", "64.0",…
## $ PeatAuto <dbl> 22, 201, 13, 5, 31, 56, 21, 13, 20, 58, 5, 12, 153, 60, …
## $ CicAuto <dbl> 5, 18, 5, 1, 11, 9, 4, 7, 2, 14, 8, 2, 54, 14, 28, 15, 2…
## $ V2RMSM <dbl> 29, 95, 2, 2, 12, 45, 8, 6, 6, 48, 4, 5, 6, 8, 1, 1, 2, …
## $ V2RMAuto <dbl> 96, 275, 14, 7, 39, 146, 21, 13, 50, 132, 8, 29, 55, 11,…
## $ AutoSM <dbl> 0, 44, 3, 2, 8, 30, 9, 1, 5, 12, 4, 5, 20, 18, 6, 7, 11,…
## $ AutoAuto <dbl> 4, 72, 1, 6, 21, 48, 12, 6, 6, 24, 3, 12, 122, 31, 11, 1…
## $ Precipitacion <chr> "565.0", "420.9", "944.1", "742.5", "831.9", "515.4", "6…
## $ Temp <chr> "18.2", "15.0", "13.8", "10.8", "12.5", "15.5", "15.1", …
## $ PBI <dbl> 31070.56, 35290.16, 35464.54, 31291.93, 48140.02, 36231.…describe(segvial)## vars n mean sd median trimmed mad
## Codigo Pais* 1 24 2.54 0.88 2.50 2.50 0.74
## Pais* 2 24 3.04 1.90 3.50 3.05 2.22
## Ciudad* 3 24 12.50 7.07 12.50 12.50 8.90
## Poblacion 4 24 976996.17 984150.14 542400.00 806857.40 355711.32
## DenPob 5 24 5483.00 4725.45 4212.50 4597.95 1490.75
## ArCiclista* 6 24 11.79 6.71 11.50 11.75 8.15
## ArBajaVel* 7 24 12.50 7.07 12.50 12.50 8.90
## PMPeatones* 8 24 6.67 3.87 7.00 6.55 4.45
## PMCiclistas* 9 24 3.71 1.57 4.00 3.65 1.48
## PMTPublico* 10 24 9.29 5.34 9.00 9.20 6.67
## PMVMotor* 11 24 10.79 6.33 10.50 10.75 8.15
## PeatAuto 12 24 70.71 93.08 36.50 52.00 35.58
## CicAuto 13 24 30.46 67.59 14.00 16.70 13.34
## V2RMSM 14 24 21.96 35.53 6.00 14.35 5.93
## V2RMAuto 15 24 74.83 106.46 29.50 53.50 25.95
## AutoSM 16 24 18.04 28.55 7.50 12.50 7.41
## AutoAuto 17 24 40.17 53.33 17.50 31.05 18.53
## Precipitacion* 18 24 12.50 7.07 12.50 12.50 8.90
## Temp* 19 24 10.79 6.40 10.50 10.80 8.15
## PBI 20 24 50517.45 31812.18 37086.79 44973.69 8503.38
## min max range skew kurtosis se
## Codigo Pais* 1.00 5 4.0 0.60 0.67 0.18
## Pais* 1.00 5 4.0 -0.06 -1.96 0.39
## Ciudad* 1.00 24 23.0 0.00 -1.35 1.44
## Poblacion 232787.00 3600203 3367416.0 1.55 0.99 200888.81
## DenPob 1417.00 20772 19355.0 1.85 2.85 964.58
## ArCiclista* 1.00 23 22.0 0.07 -1.34 1.37
## ArBajaVel* 1.00 24 23.0 0.00 -1.35 1.44
## PMPeatones* 1.00 14 13.0 0.11 -1.14 0.79
## PMCiclistas* 1.00 7 6.0 0.14 -0.98 0.32
## PMTPublico* 1.00 18 17.0 0.12 -1.31 1.09
## PMVMotor* 1.00 21 20.0 0.07 -1.45 1.29
## PeatAuto 5.00 398 393.0 2.14 4.18 19.00
## CicAuto 1.00 340 339.0 3.98 15.34 13.80
## V2RMSM 1.00 143 142.0 2.06 3.58 7.25
## V2RMAuto 2.00 371 369.0 1.78 1.84 21.73
## AutoSM 0.00 138 138.0 3.09 10.09 5.83
## AutoAuto 1.00 217 216.0 1.81 2.73 10.89
## Precipitacion* 1.00 24 23.0 0.00 -1.35 1.44
## Temp* 1.00 21 20.0 0.01 -1.39 1.31
## PBI 23867.44 152178 128310.6 1.83 2.40 6493.63summary = summary(segvial)
summary## Codigo Pais Pais Ciudad Poblacion
## Length:24 Length:24 Length:24 Min. : 232787
## Class :character Class :character Class :character 1st Qu.: 432558
## Mode :character Mode :character Mode :character Median : 542400
## Mean : 976996
## 3rd Qu.: 932826
## Max. :3600203
## DenPob ArCiclista ArBajaVel PMPeatones
## Min. : 1417 Length:24 Length:24 Length:24
## 1st Qu.: 3223 Class :character Class :character Class :character
## Median : 4212 Mode :character Mode :character Mode :character
## Mean : 5483
## 3rd Qu.: 5161
## Max. :20772
## PMCiclistas PMTPublico PMVMotor PeatAuto
## Length:24 Length:24 Length:24 Min. : 5.00
## Class :character Class :character Class :character 1st Qu.: 18.25
## Mode :character Mode :character Mode :character Median : 36.50
## Mean : 70.71
## 3rd Qu.: 67.25
## Max. :398.00
## CicAuto V2RMSM V2RMAuto AutoSM
## Min. : 1.00 Min. : 1.00 Min. : 2.00 Min. : 0.00
## 1st Qu.: 5.75 1st Qu.: 3.50 1st Qu.: 15.50 1st Qu.: 4.75
## Median : 14.00 Median : 6.00 Median : 29.50 Median : 7.50
## Mean : 30.46 Mean : 21.96 Mean : 74.83 Mean : 18.04
## 3rd Qu.: 27.25 3rd Qu.: 20.00 3rd Qu.: 65.25 3rd Qu.: 21.00
## Max. :340.00 Max. :143.00 Max. :371.00 Max. :138.00
## AutoAuto Precipitacion Temp PBI
## Min. : 1.00 Length:24 Length:24 Min. : 23867
## 1st Qu.: 6.00 Class :character Class :character 1st Qu.: 34399
## Median : 17.50 Mode :character Mode :character Median : 37087
## Mean : 40.17 Mean : 50517
## 3rd Qu.: 49.25 3rd Qu.: 48993
## Max. :217.00 Max. :152178Contamos con 20 columnas y 24 filas,de las cuales 3 son categoricas y el resto numericas, sin embargo figuran como de tipo varchar, por lo que las trasnformaremos a numericas. Los índices presentan una gran difernecia en cuanto a magnitud entre su media, minimo y máximo. El resto de las variables pareciera tener distirbuciones similares.
segvial <- transform(segvial,
ArCiclista = as.numeric(ArCiclista),
ArBajaVel = as.numeric(ArBajaVel),
PMPeatones = as.numeric(PMPeatones),
PMCiclistas = as.numeric(PMCiclistas),
PMTPublico = as.numeric(PMTPublico),
PMVMotor = as.numeric(PMVMotor),
Precipitacion = as.numeric(Precipitacion),
Temp = as.numeric(Temp))
segvial## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 16014 0.150
## 2 ES Spain Madrid 3223334 5332 0.067
## 3 FR France Bordeaux 254436 5104 0.106
## 4 FR France Lille 232787 6687 0.098
## 5 FR France Lyon 516092 10758 0.109
## 6 FR France Marseille 863310 3564 0.044
## 7 FR France Montpellier 285121 4997 0.167
## 8 FR France Nantes 309346 4704 0.327
## 9 FR France Nice 340017 4586 0.056
## 10 FR France Paris 2187526 20772 0.186
## 11 FR France Strasbourg 280966 3591 0.333
## 12 FR France Toulouse 479553 4064 0.211
## 13 GB United Kingdom Birmingham 1141374 4262 0.074
## 14 GB United Kingdom Bradford 537173 1465 0.030
## 15 GB United Kingdom Bristol 463405 4163 0.107
## 16 GB United Kingdom Edinburgh 518500 1899 0.119
## 17 GB United Kingdom Glasgow 626410 3553 0.067
## 18 GB United Kingdom Leeds 789194 1430 0.085
## 19 GB United Kingdom Liverpool 494814 3704 0.033
## 20 GB United Kingdom Inner London 3600203 10975 0.108
## 21 GB United Kingdom Manchester 547627 4735 0.082
## 22 GB United Kingdom Sheffield 582506 1583 0.053
## 23 IT Italy Rome 2872800 2233 0.027
## 24 NO Norway Oslo 681071 1417 0.163
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto V2RMSM
## 1 0.775 32.0 2.00 39.00 27.00 22 5 29
## 2 0.065 34.0 0.46 24.35 39.89 201 18 95
## 3 0.239 21.0 3.00 9.00 67.00 13 5 2
## 4 0.634 32.0 2.00 10.00 56.00 5 1 2
## 5 0.284 34.0 2.00 19.00 45.00 31 11 12
## 6 0.118 34.0 1.00 11.00 54.00 56 9 45
## 7 0.125 26.0 2.00 8.00 64.00 21 4 8
## 8 0.354 27.0 5.00 16.00 52.00 13 7 6
## 9 0.102 40.0 1.00 11.00 48.00 20 2 6
## 10 0.491 47.0 3.00 33.00 17.00 58 14 48
## 11 0.187 33.0 8.00 12.00 47.00 5 8 4
## 12 0.385 21.0 3.00 9.00 67.00 12 2 5
## 13 0.186 10.0 2.00 18.00 70.00 153 54 6
## 14 0.024 5.0 1.00 31.00 63.00 60 14 8
## 15 0.460 21.0 14.00 16.00 49.00 25 28 1
## 16 0.745 34.0 2.00 19.00 45.00 33 15 1
## 17 0.095 27.0 1.00 33.00 39.00 61 23 2
## 18 0.058 4.0 1.00 38.00 57.00 75 50 17
## 19 0.242 12.0 2.00 26.00 61.00 71 27 5
## 20 0.871 25.0 3.00 36.00 37.00 398 340 72
## 21 0.393 10.0 2.00 17.00 71.00 40 34 4
## 22 0.039 10.0 1.00 34.00 55.00 66 39 4
## 23 0.029 4.6 1.00 28.90 65.50 249 15 143
## 24 0.237 32.0 5.00 26.00 37.00 9 6 2
## V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 96 0 4 565.0 18.2 31070.56
## 2 275 44 72 420.9 15.0 35290.16
## 3 14 3 1 944.1 13.8 35464.54
## 4 7 2 6 742.5 10.8 31291.93
## 5 39 8 21 831.9 12.5 48140.02
## 6 146 30 48 515.4 15.5 36231.61
## 7 21 9 12 629.1 15.1 152178.01
## 8 13 1 6 819.5 12.5 36188.05
## 9 50 5 6 733.0 16.0 36217.14
## 10 132 12 24 637.4 12.4 109123.01
## 11 8 4 3 665.0 10.9 34824.27
## 12 29 5 12 638.0 14.3 42762.83
## 13 55 20 122 804.9 9.7 29764.20
## 14 11 18 31 872.8 9.4 23867.44
## 15 22 6 11 802.1 10.6 39810.18
## 16 17 7 14 704.3 9.3 55206.04
## 17 16 11 14 1245.3 9.6 42480.23
## 18 43 24 53 809.9 9.4 41428.04
## 19 22 7 22 836.6 10.1 33121.24
## 20 371 44 123 601.7 11.3 101968.91
## 21 31 5 28 828.8 10.0 51550.57
## 22 30 28 112 834.6 10.0 27776.48
## 23 346 138 217 798.5 15.3 37941.96
## 24 2 2 2 802.7 6.8 98721.31Categorización de variables
Las variables pueden ser categorizadas en cuatro categorías:
Métrica contempla todos los inidices MDS
Ambientales consideramos temas de ambiente como la tempratura y las lluvias
Demográficas es relativo a población
Geográfica son las que representan país o ciudad
Características urbanas son aquellas relativas a áreas o espacio de los diferentes agentes viales
Curiosidades
El objetivo es encontrar vulnerabilidad o ciertas características que nos permitan tomar medidas de seguridad vial, por lo que surgen las siguientes inquietudes que buscaremos resolver:
En aquellas ciudades que llueve más ¿se generan mayor cantidad de accidentes tanto por si mismo como entre otros?
Las ciudades que tienen menos espacio para ciclistas presentan mayores accidentes
Si la temperatura promedio es agradable hay mayores accidentes en bicicleta o peaton
Existe alguna relacion con el área o participación y sus accidentes
Aquellas ciudades que tienen mayores zonas de baja velocidad que indices presentan
Buscaremos dar respuesta a todas estas inquietudes de manera gráfica.
Análisis gráfico
Lluvia vs MSD
ggplot(segvial, aes(x = PeatAuto, y = Precipitacion, color = Precipitacion)) +
geom_point()
ggplot(segvial, aes(x = CicAuto, y = Precipitacion, color = Precipitacion)) +
geom_point()
ggplot(segvial, aes(x = V2RMSM, y = Precipitacion, color = Precipitacion)) +
geom_point()
ggplot(segvial, aes(x = V2RMAuto, y = Precipitacion, color = Precipitacion)) +
geom_point()
ggplot(segvial, aes(x = AutoSM, y = Precipitacion, color = Precipitacion)) +
geom_point()
ggplot(segvial, aes(x = AutoAuto, y = Precipitacion, color = Precipitacion)) +
geom_point()
No pareciera que aquellos lugares donde más llueve presenten los mayores indices.
Ciudades vs espacio ciclista
ggplot(segvial, aes(x = CicAuto, y = ArCiclista, color = Pais)) +
geom_point()
ggplot(segvial, aes(x = CicAuto, y = PMCiclistas, color = Pais)) +
geom_point()
No pareciera que se puedan identificar clusters particulares de un único país si podemos observar que hay un valor para el indice CicAuto de reino unido cuya magnitud esta muy por encima del resto
Temparatura vs Accidentes bicicleta o peaton
ggplot(segvial, aes(x = Temp, y = CicAuto)) +
geom_point()
ggplot(segvial, aes(x = Temp, y = PeatAuto)) +
geom_point()
Pareciera que si la temperatura es agradable la mayor cantidad del año se produzcan más accidentes en bicicleta o peatonal, lo cual hae sentido si pensamos que la gente saldría más de su hogar a realizar alguna actividad.
Participacion vs indice
ggplot(segvial, aes(x = PeatAuto, y = PMVMotor, color = PMVMotor)) +
geom_point()
ggplot(segvial, aes(x = CicAuto, y = PMCiclistas, color = PMCiclistas)) +
geom_point()
ggplot(segvial, aes(x = V2RMSM, y = PMVMotor, color = PMVMotor)) +
geom_point()
ggplot(segvial, aes(x = V2RMAuto, y = PMVMotor, color = PMVMotor)) +
geom_point()
ggplot(segvial, aes(x = AutoSM, y = PMVMotor, color = PMVMotor)) +
geom_point()
ggplot(segvial, aes(x = AutoAuto, y = PMVMotor, color = PMVMotor)) +
geom_point()
La participación que tiene el espacio si se trata de un vehiculo, peaton o ciclista pareciera que tiene cierta inferencia en los indices de accidentes, donde en algunos casos se puede ver que a mayor cantidad es más grande el indice.
Zona de baja velocidad vs indice
ggplot(segvial, aes(x = PeatAuto, y = ArBajaVel, color = ArBajaVel)) +
geom_point()
ggplot(segvial, aes(x = CicAuto, y = ArBajaVel, color = ArBajaVel)) +
geom_point()
ggplot(segvial, aes(x = V2RMSM, y = ArBajaVel, color = ArBajaVel)) +
geom_point()
ggplot(segvial, aes(x = V2RMAuto, y = ArBajaVel, color = ArBajaVel)) +
geom_point()
ggplot(segvial, aes(x = AutoSM, y = ArBajaVel, color = ArBajaVel)) +
geom_point()
ggplot(segvial, aes(x = AutoAuto, y = ArBajaVel, color = Precipitacion)) +
geom_point()
Se puede observar principalmente en quellos graficos que muestran indices de vehículos que hay mayores indices para algunas ciudades, también que en aquellas ciudades que tienen mayores porcentajes de áreas destinadas de baja velocidad presentan menores indicaroes para peaton y ciclista.
Histogramas
Utilizamos histogramas para ver la distribución de las variables de respuesta.
# PeatAuto CicAuto V2RMSM V2RMAut AutoSM AutoAuto
hp <- segvial %>%
ggplot( aes(x=PeatAuto)) +
geom_histogram( binwidth=3, fill="#69b3a2", color="#e9ecef", alpha=0.9) +
ggtitle("Distribucion de PeatAuto") +
theme_ipsum() +
theme(
plot.title = element_text(size=8)
)
hc <- segvial %>%
ggplot( aes(x=CicAuto)) +
geom_histogram( binwidth=3, fill="#9a76db", color="#e9ecef", alpha=0.9) +
ggtitle("Distribucion de CicAuto") +
theme_ipsum() +
theme(
plot.title = element_text(size=8)
)
hv2rm <- segvial %>%
ggplot( aes(x=V2RMSM)) +
geom_histogram( binwidth=3, fill="#60bd88", color="#e9ecef", alpha=0.9) +
ggtitle("Distribucion del V2RMSM") +
theme_ipsum() +
theme(
plot.title = element_text(size=8)
)
hv2a <- segvial %>%
ggplot( aes(x=V2RMAuto)) +
geom_histogram( binwidth=3, fill="#609ebd", color="#e9ecef", alpha=0.9) +
ggtitle("Distribucion de V2RMAuto") +
theme_ipsum() +
theme(
plot.title = element_text(size=8)
)
has <- segvial %>%
ggplot( aes(x=AutoSM)) +
geom_histogram( binwidth=3, fill="#609ebd", color="#e9ecef", alpha=0.9) +
ggtitle("Distribucion de AutoSM") +
theme_ipsum() +
theme(
plot.title = element_text(size=8)
)
haa <- segvial %>%
ggplot( aes(x=AutoAuto)) +
geom_histogram( binwidth=3, fill="#609ebd", color="#e9ecef", alpha=0.9) +
ggtitle("Distribucion de AutoAuto") +
theme_ipsum() +
theme(
plot.title = element_text(size=8)
)
ggarrange(hp,hc,hv2rm,hv2a,has,haa,
nrow = 1,
ncol = 2
)## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family
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##
## $`2`
##
## $`3`
##
## attr(,"class")
## [1] "list" "ggarrange"Correlación General
Vamos a realizar una matriz de correlación general considerando todas las variables
ggcorr(segvial, method = c("everything", "pearson"))## Warning in ggcorr(segvial, method = c("everything", "pearson")): data in
## column(s) 'Codigo.Pais', 'Pais', 'Ciudad' are not numeric and were ignored
Existen varias correlaciones fuertes que podrían estar indicando posible colieanidad.
Estandarización de variables
Variables respuesta
segvial$stPeatAuto <- PeatAuto/Poblacion*(10^6)
segvial$stCicAuto <- CicAuto/Poblacion*(10^6)
segvial$stV2RMSM <- V2RMSM/Poblacion*(10^6)
segvial$stV2RMAuto <- V2RMAuto/Poblacion*(10^6)
segvial$stAutoSM <- AutoSM/Poblacion*(10^6)
segvial$stAutoAuto <- AutoAuto/Poblacion*(10^6)
segvial## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 16014 0.150
## 2 ES Spain Madrid 3223334 5332 0.067
## 3 FR France Bordeaux 254436 5104 0.106
## 4 FR France Lille 232787 6687 0.098
## 5 FR France Lyon 516092 10758 0.109
## 6 FR France Marseille 863310 3564 0.044
## 7 FR France Montpellier 285121 4997 0.167
## 8 FR France Nantes 309346 4704 0.327
## 9 FR France Nice 340017 4586 0.056
## 10 FR France Paris 2187526 20772 0.186
## 11 FR France Strasbourg 280966 3591 0.333
## 12 FR France Toulouse 479553 4064 0.211
## 13 GB United Kingdom Birmingham 1141374 4262 0.074
## 14 GB United Kingdom Bradford 537173 1465 0.030
## 15 GB United Kingdom Bristol 463405 4163 0.107
## 16 GB United Kingdom Edinburgh 518500 1899 0.119
## 17 GB United Kingdom Glasgow 626410 3553 0.067
## 18 GB United Kingdom Leeds 789194 1430 0.085
## 19 GB United Kingdom Liverpool 494814 3704 0.033
## 20 GB United Kingdom Inner London 3600203 10975 0.108
## 21 GB United Kingdom Manchester 547627 4735 0.082
## 22 GB United Kingdom Sheffield 582506 1583 0.053
## 23 IT Italy Rome 2872800 2233 0.027
## 24 NO Norway Oslo 681071 1417 0.163
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto V2RMSM
## 1 0.775 32.0 2.00 39.00 27.00 22 5 29
## 2 0.065 34.0 0.46 24.35 39.89 201 18 95
## 3 0.239 21.0 3.00 9.00 67.00 13 5 2
## 4 0.634 32.0 2.00 10.00 56.00 5 1 2
## 5 0.284 34.0 2.00 19.00 45.00 31 11 12
## 6 0.118 34.0 1.00 11.00 54.00 56 9 45
## 7 0.125 26.0 2.00 8.00 64.00 21 4 8
## 8 0.354 27.0 5.00 16.00 52.00 13 7 6
## 9 0.102 40.0 1.00 11.00 48.00 20 2 6
## 10 0.491 47.0 3.00 33.00 17.00 58 14 48
## 11 0.187 33.0 8.00 12.00 47.00 5 8 4
## 12 0.385 21.0 3.00 9.00 67.00 12 2 5
## 13 0.186 10.0 2.00 18.00 70.00 153 54 6
## 14 0.024 5.0 1.00 31.00 63.00 60 14 8
## 15 0.460 21.0 14.00 16.00 49.00 25 28 1
## 16 0.745 34.0 2.00 19.00 45.00 33 15 1
## 17 0.095 27.0 1.00 33.00 39.00 61 23 2
## 18 0.058 4.0 1.00 38.00 57.00 75 50 17
## 19 0.242 12.0 2.00 26.00 61.00 71 27 5
## 20 0.871 25.0 3.00 36.00 37.00 398 340 72
## 21 0.393 10.0 2.00 17.00 71.00 40 34 4
## 22 0.039 10.0 1.00 34.00 55.00 66 39 4
## 23 0.029 4.6 1.00 28.90 65.50 249 15 143
## 24 0.237 32.0 5.00 26.00 37.00 9 6 2
## V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI stPeatAuto stCicAuto
## 1 96 0 4 565.0 18.2 31070.56 13.57737 3.085766
## 2 275 44 72 420.9 15.0 35290.16 62.35779 5.584280
## 3 14 3 1 944.1 13.8 35464.54 51.09340 19.651307
## 4 7 2 6 742.5 10.8 31291.93 21.47886 4.295773
## 5 39 8 21 831.9 12.5 48140.02 60.06681 21.314029
## 6 146 30 48 515.4 15.5 36231.61 64.86662 10.424992
## 7 21 9 12 629.1 15.1 152178.01 73.65294 14.029131
## 8 13 1 6 819.5 12.5 36188.05 42.02414 22.628384
## 9 50 5 6 733.0 16.0 36217.14 58.82059 5.882059
## 10 132 12 24 637.4 12.4 109123.01 26.51397 6.399924
## 11 8 4 3 665.0 10.9 34824.27 17.79575 28.473196
## 12 29 5 12 638.0 14.3 42762.83 25.02330 4.170550
## 13 55 20 122 804.9 9.7 29764.20 134.04896 47.311398
## 14 11 18 31 872.8 9.4 23867.44 111.69586 26.062367
## 15 22 6 11 802.1 10.6 39810.18 53.94849 60.422309
## 16 17 7 14 704.3 9.3 55206.04 63.64513 28.929605
## 17 16 11 14 1245.3 9.6 42480.23 97.38031 36.717166
## 18 43 24 53 809.9 9.4 41428.04 95.03367 63.355778
## 19 22 7 22 836.6 10.1 33121.24 143.48826 54.565958
## 20 371 44 123 601.7 11.3 101968.91 110.54932 94.439119
## 21 31 5 28 828.8 10.0 51550.57 73.04242 62.086055
## 22 30 28 112 834.6 10.0 27776.48 113.30355 66.952100
## 23 346 138 217 798.5 15.3 37941.96 86.67502 5.221387
## 24 2 2 2 802.7 6.8 98721.31 13.21448 8.809654
## stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 17.897445 59.246715 0.000000 2.468613
## 2 29.472590 85.315391 13.650463 22.337121
## 3 7.860523 55.023660 11.790784 3.930261
## 4 8.591545 30.070408 8.591545 25.774635
## 5 23.251668 75.567922 15.501112 40.690420
## 6 52.124961 169.116540 34.749974 55.599958
## 7 28.058263 73.652940 31.565546 42.087394
## 8 19.395758 42.024141 3.232626 19.395758
## 9 17.646177 147.051471 14.705147 17.646177
## 10 21.942596 60.342140 5.485649 10.971298
## 11 14.236598 28.473196 14.236598 10.677449
## 12 10.426376 60.472982 10.426376 25.023303
## 13 5.256822 48.187535 17.522740 106.888715
## 14 14.892781 20.477574 33.508758 57.709527
## 15 2.157940 47.474671 12.947638 23.737336
## 16 1.928640 32.786885 13.500482 27.000964
## 17 3.192797 25.542376 17.560384 22.349579
## 18 21.540965 54.485969 30.410774 67.157125
## 19 10.104807 44.461151 14.146730 44.461151
## 20 19.998872 103.049745 12.221533 34.164740
## 21 7.304242 56.607874 9.130302 51.129692
## 22 6.866882 51.501615 48.068174 192.272698
## 23 49.777221 120.439989 48.036759 75.536062
## 24 2.936551 2.936551 2.936551 2.936551Variables regresoras
DenPob,ArCiclista,ArBajaVel,PMPeatones,PMCiclistas,PMTPublico,PMVMotor,Temp,Precipitacion,PBI
df_segvial <- segvial%>% mutate_at (c ('Precipitacion', 'PBI', 'Temp','PMVMotor','PMTPublico','PMCiclistas','PMPeatones','ArBajaVel','ArCiclista','DenPob'), ~ ( scale (.)%>% as.vector ))
df_segvial## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 2.22857298 0.40404755
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 3 FR France Bordeaux 254436 -0.08020408 -0.13063191
## 4 FR France Lille 232787 0.25479080 -0.22784636
## 5 FR France Lyon 516092 1.11629688 -0.09417650
## 6 FR France Marseille 863310 -0.40609928 -0.88404389
## 7 FR France Montpellier 285121 -0.10284745 0.61062825
## 8 FR France Nantes 309346 -0.16485218 2.55491722
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 10 FR France Paris 2187526 3.23546219 0.84151257
## 11 FR France Strasbourg 280966 -0.40038554 2.62782805
## 12 FR France Toulouse 479553 -0.30028915 1.14530772
## 13 GB United Kingdom Birmingham 1141374 -0.25838834 -0.51948971
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 15 GB United Kingdom Bristol 463405 -0.27933875 -0.11848011
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 17 GB United Kingdom Glasgow 626410 -0.40842711 -0.60455235
## 18 GB United Kingdom Leeds 789194 -0.85769692 -0.38581984
## 19 GB United Kingdom Liverpool 494814 -0.37647245 -1.01771375
## 20 GB United Kingdom Inner London 3600203 1.16221848 -0.10632830
## 21 GB United Kingdom Manchester 547627 -0.15829196 -0.42227526
## 22 GB United Kingdom Sheffield 582506 -0.82531902 -0.77467763
## 23 IT Italy Rome 2872800 -0.68776585 -1.09062459
## 24 NO Norway Oslo 681071 -0.86044799 0.56202103
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 1 1.8984595 0.66156039 -0.27804757 1.6644700 -1.74922903 22 5
## 2 -0.9238882 0.82660664 -0.80613791 0.2431521 -0.82481711 201 18
## 3 -0.2322143 -0.24619399 0.06486824 -1.2460786 1.11938825 13 5
## 4 1.3379651 0.66156039 -0.27804757 -1.1490604 0.33051850 5 1
## 5 -0.0533331 0.82660664 -0.27804757 -0.2758958 -0.45835126 31 11
## 6 -0.7132059 0.82660664 -0.62096337 -1.0520421 0.18708763 56 9
## 7 -0.6853800 0.16642164 -0.27804757 -1.3430969 0.90424195 21 4
## 8 0.2249265 0.24894476 0.75069985 -0.5669506 0.04365677 13 7
## 9 -0.7768081 1.32174540 -0.62096337 -1.0520421 -0.24320496 20 2
## 10 0.7695204 1.89940728 0.06486824 1.0823603 -2.46638335 58 14
## 11 -0.4389214 0.74408352 1.77944727 -0.9550238 -0.31492039 5 8
## 12 0.3481558 -0.24619399 0.06486824 -1.2460786 1.11938825 12 2
## 13 -0.4428966 -1.15394837 -0.27804757 -0.3729140 1.33453455 153 54
## 14 -1.0868689 -1.56656400 -0.62096337 0.8883237 0.83252652 60 14
## 15 0.6462911 -0.24619399 3.83694212 -0.5669506 -0.17148953 25 28
## 16 1.7792053 0.82660664 -0.27804757 -0.2758958 -0.45835126 33 15
## 17 -0.8046341 0.24894476 -0.62096337 1.0823603 -0.88864385 61 23
## 18 -0.9517142 -1.64908713 -0.62096337 1.5674517 0.40223393 75 50
## 19 -0.2202889 -0.98890212 -0.27804757 0.4032323 0.68909566 71 27
## 20 2.2800727 0.08389851 0.06486824 1.3734151 -1.03207471 398 340
## 21 0.3799569 -1.15394837 -0.27804757 -0.4699323 1.40624998 40 34
## 22 -1.0272418 -1.15394837 -0.62096337 1.1793786 0.25880307 66 39
## 23 -1.0669932 -1.59957325 -0.62096337 0.6845853 1.01181510 249 15
## 24 -0.2401646 0.66156039 0.75069985 0.4032323 -1.03207471 9 6
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 29 96 0 4 -1.15398383 2.2028201 -0.61130324
## 2 95 275 44 72 -2.03615396 1.0620475 -0.47866210
## 3 2 14 3 1 1.16683988 0.6342577 -0.47318053
## 4 2 7 2 6 -0.06734123 -0.4352167 -0.60434454
## 5 12 39 8 21 0.47995932 0.1708188 -0.07473309
## 6 45 146 30 48 -1.45763156 1.2402932 -0.44906814
## 7 8 21 9 12 -0.76156811 1.0976966 3.19564902
## 8 6 13 1 6 0.40404739 0.1708188 -0.45043750
## 9 6 50 5 6 -0.12549957 1.4185389 -0.44952287
## 10 48 132 12 24 -0.71075609 0.1351697 1.84223680
## 11 4 8 4 3 -0.54179082 -0.3995675 -0.49330720
## 12 5 29 5 12 -0.70708293 0.8125034 -0.24376255
## 13 6 55 20 122 0.31466721 -0.8273573 -0.65236796
## 14 8 11 18 31 0.73034626 -0.9343047 -0.83772977
## 15 1 22 6 11 0.29752580 -0.5065149 -0.33657759
## 16 1 17 7 14 -0.30119896 -0.9699538 0.14738356
## 17 2 16 11 14 3.01076524 -0.8630064 -0.25264595
## 18 17 43 24 53 0.34527686 -0.9343047 -0.28572111
## 19 5 22 7 22 0.50873239 -0.6847607 -0.54684114
## 20 72 371 44 123 -0.92930899 -0.2569709 1.61735105
## 21 4 31 5 28 0.46098134 -0.7204098 0.03247566
## 22 4 30 28 112 0.49648853 -0.7204098 -0.71485101
## 23 143 346 138 217 0.27548686 1.1689949 -0.39530410
## 24 2 2 2 2 0.30119896 -1.8611825 1.51526431
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 13.57737 3.085766 17.897445 59.246715 0.000000 2.468613
## 2 62.35779 5.584280 29.472590 85.315391 13.650463 22.337121
## 3 51.09340 19.651307 7.860523 55.023660 11.790784 3.930261
## 4 21.47886 4.295773 8.591545 30.070408 8.591545 25.774635
## 5 60.06681 21.314029 23.251668 75.567922 15.501112 40.690420
## 6 64.86662 10.424992 52.124961 169.116540 34.749974 55.599958
## 7 73.65294 14.029131 28.058263 73.652940 31.565546 42.087394
## 8 42.02414 22.628384 19.395758 42.024141 3.232626 19.395758
## 9 58.82059 5.882059 17.646177 147.051471 14.705147 17.646177
## 10 26.51397 6.399924 21.942596 60.342140 5.485649 10.971298
## 11 17.79575 28.473196 14.236598 28.473196 14.236598 10.677449
## 12 25.02330 4.170550 10.426376 60.472982 10.426376 25.023303
## 13 134.04896 47.311398 5.256822 48.187535 17.522740 106.888715
## 14 111.69586 26.062367 14.892781 20.477574 33.508758 57.709527
## 15 53.94849 60.422309 2.157940 47.474671 12.947638 23.737336
## 16 63.64513 28.929605 1.928640 32.786885 13.500482 27.000964
## 17 97.38031 36.717166 3.192797 25.542376 17.560384 22.349579
## 18 95.03367 63.355778 21.540965 54.485969 30.410774 67.157125
## 19 143.48826 54.565958 10.104807 44.461151 14.146730 44.461151
## 20 110.54932 94.439119 19.998872 103.049745 12.221533 34.164740
## 21 73.04242 62.086055 7.304242 56.607874 9.130302 51.129692
## 22 113.30355 66.952100 6.866882 51.501615 48.068174 192.272698
## 23 86.67502 5.221387 49.777221 120.439989 48.036759 75.536062
## 24 13.21448 8.809654 2.936551 2.936551 2.936551 2.936551Modelado
Datos
Para poder evaluar la capacidad predictiva de cada modelo, se dividen las observaciones disponibles en dos grupos: uno de entrenamiento y otro de test, para cada variable de respuesta.
PeatAuto
split <- sample.split(df_segvial$stPeatAuto, SplitRatio = 2/3)
training_set_pa <- subset(df_segvial, split==T)
test_set_pa <- subset(df_segvial, split==F)
training_set_pa## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 3 FR France Bordeaux 254436 -0.08020408 -0.13063191
## 4 FR France Lille 232787 0.25479080 -0.22784636
## 5 FR France Lyon 516092 1.11629688 -0.09417650
## 6 FR France Marseille 863310 -0.40609928 -0.88404389
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 10 FR France Paris 2187526 3.23546219 0.84151257
## 13 GB United Kingdom Birmingham 1141374 -0.25838834 -0.51948971
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 15 GB United Kingdom Bristol 463405 -0.27933875 -0.11848011
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 17 GB United Kingdom Glasgow 626410 -0.40842711 -0.60455235
## 18 GB United Kingdom Leeds 789194 -0.85769692 -0.38581984
## 21 GB United Kingdom Manchester 547627 -0.15829196 -0.42227526
## 22 GB United Kingdom Sheffield 582506 -0.82531902 -0.77467763
## 24 NO Norway Oslo 681071 -0.86044799 0.56202103
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 2 -0.9238882 0.8266066 -0.80613791 0.2431521 -0.8248171 201 18
## 3 -0.2322143 -0.2461940 0.06486824 -1.2460786 1.1193883 13 5
## 4 1.3379651 0.6615604 -0.27804757 -1.1490604 0.3305185 5 1
## 5 -0.0533331 0.8266066 -0.27804757 -0.2758958 -0.4583513 31 11
## 6 -0.7132059 0.8266066 -0.62096337 -1.0520421 0.1870876 56 9
## 9 -0.7768081 1.3217454 -0.62096337 -1.0520421 -0.2432050 20 2
## 10 0.7695204 1.8994073 0.06486824 1.0823603 -2.4663834 58 14
## 13 -0.4428966 -1.1539484 -0.27804757 -0.3729140 1.3345345 153 54
## 14 -1.0868689 -1.5665640 -0.62096337 0.8883237 0.8325265 60 14
## 15 0.6462911 -0.2461940 3.83694212 -0.5669506 -0.1714895 25 28
## 16 1.7792053 0.8266066 -0.27804757 -0.2758958 -0.4583513 33 15
## 17 -0.8046341 0.2489448 -0.62096337 1.0823603 -0.8886438 61 23
## 18 -0.9517142 -1.6490871 -0.62096337 1.5674517 0.4022339 75 50
## 21 0.3799569 -1.1539484 -0.27804757 -0.4699323 1.4062500 40 34
## 22 -1.0272418 -1.1539484 -0.62096337 1.1793786 0.2588031 66 39
## 24 -0.2401646 0.6615604 0.75069985 0.4032323 -1.0320747 9 6
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 2 95 275 44 72 -2.03615396 1.0620475 -0.47866210
## 3 2 14 3 1 1.16683988 0.6342577 -0.47318053
## 4 2 7 2 6 -0.06734123 -0.4352167 -0.60434454
## 5 12 39 8 21 0.47995932 0.1708188 -0.07473309
## 6 45 146 30 48 -1.45763156 1.2402932 -0.44906814
## 9 6 50 5 6 -0.12549957 1.4185389 -0.44952287
## 10 48 132 12 24 -0.71075609 0.1351697 1.84223680
## 13 6 55 20 122 0.31466721 -0.8273573 -0.65236796
## 14 8 11 18 31 0.73034626 -0.9343047 -0.83772977
## 15 1 22 6 11 0.29752580 -0.5065149 -0.33657759
## 16 1 17 7 14 -0.30119896 -0.9699538 0.14738356
## 17 2 16 11 14 3.01076524 -0.8630064 -0.25264595
## 18 17 43 24 53 0.34527686 -0.9343047 -0.28572111
## 21 4 31 5 28 0.46098134 -0.7204098 0.03247566
## 22 4 30 28 112 0.49648853 -0.7204098 -0.71485101
## 24 2 2 2 2 0.30119896 -1.8611825 1.51526431
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 2 62.35779 5.584280 29.472590 85.315391 13.650463 22.337121
## 3 51.09340 19.651307 7.860523 55.023660 11.790784 3.930261
## 4 21.47886 4.295773 8.591545 30.070408 8.591545 25.774635
## 5 60.06681 21.314029 23.251668 75.567922 15.501112 40.690420
## 6 64.86662 10.424992 52.124961 169.116540 34.749974 55.599958
## 9 58.82059 5.882059 17.646177 147.051471 14.705147 17.646177
## 10 26.51397 6.399924 21.942596 60.342140 5.485649 10.971298
## 13 134.04896 47.311398 5.256822 48.187535 17.522740 106.888715
## 14 111.69586 26.062367 14.892781 20.477574 33.508758 57.709527
## 15 53.94849 60.422309 2.157940 47.474671 12.947638 23.737336
## 16 63.64513 28.929605 1.928640 32.786885 13.500482 27.000964
## 17 97.38031 36.717166 3.192797 25.542376 17.560384 22.349579
## 18 95.03367 63.355778 21.540965 54.485969 30.410774 67.157125
## 21 73.04242 62.086055 7.304242 56.607874 9.130302 51.129692
## 22 113.30355 66.952100 6.866882 51.501615 48.068174 192.272698
## 24 13.21448 8.809654 2.936551 2.936551 2.936551 2.936551CicAuto
split <- sample.split(df_segvial$stCicAuto, SplitRatio = 2/3)
training_set_ca <- subset(df_segvial, split==T)
test_set_ca <- subset(df_segvial, split==F)
training_set_ca## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 2.22857298 0.40404755
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 3 FR France Bordeaux 254436 -0.08020408 -0.13063191
## 4 FR France Lille 232787 0.25479080 -0.22784636
## 8 FR France Nantes 309346 -0.16485218 2.55491722
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 10 FR France Paris 2187526 3.23546219 0.84151257
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 15 GB United Kingdom Bristol 463405 -0.27933875 -0.11848011
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 17 GB United Kingdom Glasgow 626410 -0.40842711 -0.60455235
## 18 GB United Kingdom Leeds 789194 -0.85769692 -0.38581984
## 19 GB United Kingdom Liverpool 494814 -0.37647245 -1.01771375
## 20 GB United Kingdom Inner London 3600203 1.16221848 -0.10632830
## 21 GB United Kingdom Manchester 547627 -0.15829196 -0.42227526
## 24 NO Norway Oslo 681071 -0.86044799 0.56202103
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 1 1.8984595 0.66156039 -0.27804757 1.6644700 -1.74922903 22 5
## 2 -0.9238882 0.82660664 -0.80613791 0.2431521 -0.82481711 201 18
## 3 -0.2322143 -0.24619399 0.06486824 -1.2460786 1.11938825 13 5
## 4 1.3379651 0.66156039 -0.27804757 -1.1490604 0.33051850 5 1
## 8 0.2249265 0.24894476 0.75069985 -0.5669506 0.04365677 13 7
## 9 -0.7768081 1.32174540 -0.62096337 -1.0520421 -0.24320496 20 2
## 10 0.7695204 1.89940728 0.06486824 1.0823603 -2.46638335 58 14
## 14 -1.0868689 -1.56656400 -0.62096337 0.8883237 0.83252652 60 14
## 15 0.6462911 -0.24619399 3.83694212 -0.5669506 -0.17148953 25 28
## 16 1.7792053 0.82660664 -0.27804757 -0.2758958 -0.45835126 33 15
## 17 -0.8046341 0.24894476 -0.62096337 1.0823603 -0.88864385 61 23
## 18 -0.9517142 -1.64908713 -0.62096337 1.5674517 0.40223393 75 50
## 19 -0.2202889 -0.98890212 -0.27804757 0.4032323 0.68909566 71 27
## 20 2.2800727 0.08389851 0.06486824 1.3734151 -1.03207471 398 340
## 21 0.3799569 -1.15394837 -0.27804757 -0.4699323 1.40624998 40 34
## 24 -0.2401646 0.66156039 0.75069985 0.4032323 -1.03207471 9 6
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 29 96 0 4 -1.15398383 2.2028201 -0.61130324
## 2 95 275 44 72 -2.03615396 1.0620475 -0.47866210
## 3 2 14 3 1 1.16683988 0.6342577 -0.47318053
## 4 2 7 2 6 -0.06734123 -0.4352167 -0.60434454
## 8 6 13 1 6 0.40404739 0.1708188 -0.45043750
## 9 6 50 5 6 -0.12549957 1.4185389 -0.44952287
## 10 48 132 12 24 -0.71075609 0.1351697 1.84223680
## 14 8 11 18 31 0.73034626 -0.9343047 -0.83772977
## 15 1 22 6 11 0.29752580 -0.5065149 -0.33657759
## 16 1 17 7 14 -0.30119896 -0.9699538 0.14738356
## 17 2 16 11 14 3.01076524 -0.8630064 -0.25264595
## 18 17 43 24 53 0.34527686 -0.9343047 -0.28572111
## 19 5 22 7 22 0.50873239 -0.6847607 -0.54684114
## 20 72 371 44 123 -0.92930899 -0.2569709 1.61735105
## 21 4 31 5 28 0.46098134 -0.7204098 0.03247566
## 24 2 2 2 2 0.30119896 -1.8611825 1.51526431
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 13.57737 3.085766 17.897445 59.246715 0.000000 2.468613
## 2 62.35779 5.584280 29.472590 85.315391 13.650463 22.337121
## 3 51.09340 19.651307 7.860523 55.023660 11.790784 3.930261
## 4 21.47886 4.295773 8.591545 30.070408 8.591545 25.774635
## 8 42.02414 22.628384 19.395758 42.024141 3.232626 19.395758
## 9 58.82059 5.882059 17.646177 147.051471 14.705147 17.646177
## 10 26.51397 6.399924 21.942596 60.342140 5.485649 10.971298
## 14 111.69586 26.062367 14.892781 20.477574 33.508758 57.709527
## 15 53.94849 60.422309 2.157940 47.474671 12.947638 23.737336
## 16 63.64513 28.929605 1.928640 32.786885 13.500482 27.000964
## 17 97.38031 36.717166 3.192797 25.542376 17.560384 22.349579
## 18 95.03367 63.355778 21.540965 54.485969 30.410774 67.157125
## 19 143.48826 54.565958 10.104807 44.461151 14.146730 44.461151
## 20 110.54932 94.439119 19.998872 103.049745 12.221533 34.164740
## 21 73.04242 62.086055 7.304242 56.607874 9.130302 51.129692
## 24 13.21448 8.809654 2.936551 2.936551 2.936551 2.936551AutoSM
split <- sample.split(df_segvial$stAutoSM , SplitRatio = 2/3)
training_set_as <- subset(df_segvial, split==T)
test_set_as <- subset(df_segvial, split==F)
training_set_as## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 2.22857298 0.40404755
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 5 FR France Lyon 516092 1.11629688 -0.09417650
## 7 FR France Montpellier 285121 -0.10284745 0.61062825
## 8 FR France Nantes 309346 -0.16485218 2.55491722
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 10 FR France Paris 2187526 3.23546219 0.84151257
## 11 FR France Strasbourg 280966 -0.40038554 2.62782805
## 12 FR France Toulouse 479553 -0.30028915 1.14530772
## 13 GB United Kingdom Birmingham 1141374 -0.25838834 -0.51948971
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 15 GB United Kingdom Bristol 463405 -0.27933875 -0.11848011
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 19 GB United Kingdom Liverpool 494814 -0.37647245 -1.01771375
## 20 GB United Kingdom Inner London 3600203 1.16221848 -0.10632830
## 21 GB United Kingdom Manchester 547627 -0.15829196 -0.42227526
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 1 1.8984595 0.66156039 -0.27804757 1.6644700 -1.74922903 22 5
## 2 -0.9238882 0.82660664 -0.80613791 0.2431521 -0.82481711 201 18
## 5 -0.0533331 0.82660664 -0.27804757 -0.2758958 -0.45835126 31 11
## 7 -0.6853800 0.16642164 -0.27804757 -1.3430969 0.90424195 21 4
## 8 0.2249265 0.24894476 0.75069985 -0.5669506 0.04365677 13 7
## 9 -0.7768081 1.32174540 -0.62096337 -1.0520421 -0.24320496 20 2
## 10 0.7695204 1.89940728 0.06486824 1.0823603 -2.46638335 58 14
## 11 -0.4389214 0.74408352 1.77944727 -0.9550238 -0.31492039 5 8
## 12 0.3481558 -0.24619399 0.06486824 -1.2460786 1.11938825 12 2
## 13 -0.4428966 -1.15394837 -0.27804757 -0.3729140 1.33453455 153 54
## 14 -1.0868689 -1.56656400 -0.62096337 0.8883237 0.83252652 60 14
## 15 0.6462911 -0.24619399 3.83694212 -0.5669506 -0.17148953 25 28
## 16 1.7792053 0.82660664 -0.27804757 -0.2758958 -0.45835126 33 15
## 19 -0.2202889 -0.98890212 -0.27804757 0.4032323 0.68909566 71 27
## 20 2.2800727 0.08389851 0.06486824 1.3734151 -1.03207471 398 340
## 21 0.3799569 -1.15394837 -0.27804757 -0.4699323 1.40624998 40 34
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 29 96 0 4 -1.1539838 2.2028201 -0.61130324
## 2 95 275 44 72 -2.0361540 1.0620475 -0.47866210
## 5 12 39 8 21 0.4799593 0.1708188 -0.07473309
## 7 8 21 9 12 -0.7615681 1.0976966 3.19564902
## 8 6 13 1 6 0.4040474 0.1708188 -0.45043750
## 9 6 50 5 6 -0.1254996 1.4185389 -0.44952287
## 10 48 132 12 24 -0.7107561 0.1351697 1.84223680
## 11 4 8 4 3 -0.5417908 -0.3995675 -0.49330720
## 12 5 29 5 12 -0.7070829 0.8125034 -0.24376255
## 13 6 55 20 122 0.3146672 -0.8273573 -0.65236796
## 14 8 11 18 31 0.7303463 -0.9343047 -0.83772977
## 15 1 22 6 11 0.2975258 -0.5065149 -0.33657759
## 16 1 17 7 14 -0.3011990 -0.9699538 0.14738356
## 19 5 22 7 22 0.5087324 -0.6847607 -0.54684114
## 20 72 371 44 123 -0.9293090 -0.2569709 1.61735105
## 21 4 31 5 28 0.4609813 -0.7204098 0.03247566
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 13.57737 3.085766 17.897445 59.24672 0.000000 2.468613
## 2 62.35779 5.584280 29.472590 85.31539 13.650463 22.337121
## 5 60.06681 21.314029 23.251668 75.56792 15.501112 40.690420
## 7 73.65294 14.029131 28.058263 73.65294 31.565546 42.087394
## 8 42.02414 22.628384 19.395758 42.02414 3.232626 19.395758
## 9 58.82059 5.882059 17.646177 147.05147 14.705147 17.646177
## 10 26.51397 6.399924 21.942596 60.34214 5.485649 10.971298
## 11 17.79575 28.473196 14.236598 28.47320 14.236598 10.677449
## 12 25.02330 4.170550 10.426376 60.47298 10.426376 25.023303
## 13 134.04896 47.311398 5.256822 48.18754 17.522740 106.888715
## 14 111.69586 26.062367 14.892781 20.47757 33.508758 57.709527
## 15 53.94849 60.422309 2.157940 47.47467 12.947638 23.737336
## 16 63.64513 28.929605 1.928640 32.78689 13.500482 27.000964
## 19 143.48826 54.565958 10.104807 44.46115 14.146730 44.461151
## 20 110.54932 94.439119 19.998872 103.04974 12.221533 34.164740
## 21 73.04242 62.086055 7.304242 56.60787 9.130302 51.129692AutoAuto
split <- sample.split(df_segvial$stAutoAuto, SplitRatio = 2/3)
training_set_aa <- subset(df_segvial, split==T)
test_set_aa <- subset(df_segvial, split==F)
training_set_aa## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 2.22857298 0.40404755
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 3 FR France Bordeaux 254436 -0.08020408 -0.13063191
## 4 FR France Lille 232787 0.25479080 -0.22784636
## 6 FR France Marseille 863310 -0.40609928 -0.88404389
## 7 FR France Montpellier 285121 -0.10284745 0.61062825
## 8 FR France Nantes 309346 -0.16485218 2.55491722
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 11 FR France Strasbourg 280966 -0.40038554 2.62782805
## 13 GB United Kingdom Birmingham 1141374 -0.25838834 -0.51948971
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 18 GB United Kingdom Leeds 789194 -0.85769692 -0.38581984
## 22 GB United Kingdom Sheffield 582506 -0.82531902 -0.77467763
## 23 IT Italy Rome 2872800 -0.68776585 -1.09062459
## 24 NO Norway Oslo 681071 -0.86044799 0.56202103
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 1 1.8984595 0.6615604 -0.27804757 1.6644700 -1.74922903 22 5
## 2 -0.9238882 0.8266066 -0.80613791 0.2431521 -0.82481711 201 18
## 3 -0.2322143 -0.2461940 0.06486824 -1.2460786 1.11938825 13 5
## 4 1.3379651 0.6615604 -0.27804757 -1.1490604 0.33051850 5 1
## 6 -0.7132059 0.8266066 -0.62096337 -1.0520421 0.18708763 56 9
## 7 -0.6853800 0.1664216 -0.27804757 -1.3430969 0.90424195 21 4
## 8 0.2249265 0.2489448 0.75069985 -0.5669506 0.04365677 13 7
## 9 -0.7768081 1.3217454 -0.62096337 -1.0520421 -0.24320496 20 2
## 11 -0.4389214 0.7440835 1.77944727 -0.9550238 -0.31492039 5 8
## 13 -0.4428966 -1.1539484 -0.27804757 -0.3729140 1.33453455 153 54
## 14 -1.0868689 -1.5665640 -0.62096337 0.8883237 0.83252652 60 14
## 16 1.7792053 0.8266066 -0.27804757 -0.2758958 -0.45835126 33 15
## 18 -0.9517142 -1.6490871 -0.62096337 1.5674517 0.40223393 75 50
## 22 -1.0272418 -1.1539484 -0.62096337 1.1793786 0.25880307 66 39
## 23 -1.0669932 -1.5995733 -0.62096337 0.6845853 1.01181510 249 15
## 24 -0.2401646 0.6615604 0.75069985 0.4032323 -1.03207471 9 6
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 29 96 0 4 -1.15398383 2.2028201 -0.6113032
## 2 95 275 44 72 -2.03615396 1.0620475 -0.4786621
## 3 2 14 3 1 1.16683988 0.6342577 -0.4731805
## 4 2 7 2 6 -0.06734123 -0.4352167 -0.6043445
## 6 45 146 30 48 -1.45763156 1.2402932 -0.4490681
## 7 8 21 9 12 -0.76156811 1.0976966 3.1956490
## 8 6 13 1 6 0.40404739 0.1708188 -0.4504375
## 9 6 50 5 6 -0.12549957 1.4185389 -0.4495229
## 11 4 8 4 3 -0.54179082 -0.3995675 -0.4933072
## 13 6 55 20 122 0.31466721 -0.8273573 -0.6523680
## 14 8 11 18 31 0.73034626 -0.9343047 -0.8377298
## 16 1 17 7 14 -0.30119896 -0.9699538 0.1473836
## 18 17 43 24 53 0.34527686 -0.9343047 -0.2857211
## 22 4 30 28 112 0.49648853 -0.7204098 -0.7148510
## 23 143 346 138 217 0.27548686 1.1689949 -0.3953041
## 24 2 2 2 2 0.30119896 -1.8611825 1.5152643
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 13.57737 3.085766 17.897445 59.246715 0.000000 2.468613
## 2 62.35779 5.584280 29.472590 85.315391 13.650463 22.337121
## 3 51.09340 19.651307 7.860523 55.023660 11.790784 3.930261
## 4 21.47886 4.295773 8.591545 30.070408 8.591545 25.774635
## 6 64.86662 10.424992 52.124961 169.116540 34.749974 55.599958
## 7 73.65294 14.029131 28.058263 73.652940 31.565546 42.087394
## 8 42.02414 22.628384 19.395758 42.024141 3.232626 19.395758
## 9 58.82059 5.882059 17.646177 147.051471 14.705147 17.646177
## 11 17.79575 28.473196 14.236598 28.473196 14.236598 10.677449
## 13 134.04896 47.311398 5.256822 48.187535 17.522740 106.888715
## 14 111.69586 26.062367 14.892781 20.477574 33.508758 57.709527
## 16 63.64513 28.929605 1.928640 32.786885 13.500482 27.000964
## 18 95.03367 63.355778 21.540965 54.485969 30.410774 67.157125
## 22 113.30355 66.952100 6.866882 51.501615 48.068174 192.272698
## 23 86.67502 5.221387 49.777221 120.439989 48.036759 75.536062
## 24 13.21448 8.809654 2.936551 2.936551 2.936551 2.936551V2RMSM
split <- sample.split(df_segvial$stV2RMSM , SplitRatio = 2/3)
training_set_v2rms <- subset(df_segvial, split==T)
test_set_v2rms <- subset(df_segvial, split==F)
training_set_v2rms## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 2.22857298 0.40404755
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 3 FR France Bordeaux 254436 -0.08020408 -0.13063191
## 5 FR France Lyon 516092 1.11629688 -0.09417650
## 6 FR France Marseille 863310 -0.40609928 -0.88404389
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 10 FR France Paris 2187526 3.23546219 0.84151257
## 11 FR France Strasbourg 280966 -0.40038554 2.62782805
## 13 GB United Kingdom Birmingham 1141374 -0.25838834 -0.51948971
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 17 GB United Kingdom Glasgow 626410 -0.40842711 -0.60455235
## 19 GB United Kingdom Liverpool 494814 -0.37647245 -1.01771375
## 20 GB United Kingdom Inner London 3600203 1.16221848 -0.10632830
## 23 IT Italy Rome 2872800 -0.68776585 -1.09062459
## 24 NO Norway Oslo 681071 -0.86044799 0.56202103
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 1 1.8984595 0.66156039 -0.27804757 1.6644700 -1.7492290 22 5
## 2 -0.9238882 0.82660664 -0.80613791 0.2431521 -0.8248171 201 18
## 3 -0.2322143 -0.24619399 0.06486824 -1.2460786 1.1193883 13 5
## 5 -0.0533331 0.82660664 -0.27804757 -0.2758958 -0.4583513 31 11
## 6 -0.7132059 0.82660664 -0.62096337 -1.0520421 0.1870876 56 9
## 9 -0.7768081 1.32174540 -0.62096337 -1.0520421 -0.2432050 20 2
## 10 0.7695204 1.89940728 0.06486824 1.0823603 -2.4663834 58 14
## 11 -0.4389214 0.74408352 1.77944727 -0.9550238 -0.3149204 5 8
## 13 -0.4428966 -1.15394837 -0.27804757 -0.3729140 1.3345345 153 54
## 14 -1.0868689 -1.56656400 -0.62096337 0.8883237 0.8325265 60 14
## 16 1.7792053 0.82660664 -0.27804757 -0.2758958 -0.4583513 33 15
## 17 -0.8046341 0.24894476 -0.62096337 1.0823603 -0.8886438 61 23
## 19 -0.2202889 -0.98890212 -0.27804757 0.4032323 0.6890957 71 27
## 20 2.2800727 0.08389851 0.06486824 1.3734151 -1.0320747 398 340
## 23 -1.0669932 -1.59957325 -0.62096337 0.6845853 1.0118151 249 15
## 24 -0.2401646 0.66156039 0.75069985 0.4032323 -1.0320747 9 6
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 29 96 0 4 -1.1539838 2.2028201 -0.61130324
## 2 95 275 44 72 -2.0361540 1.0620475 -0.47866210
## 3 2 14 3 1 1.1668399 0.6342577 -0.47318053
## 5 12 39 8 21 0.4799593 0.1708188 -0.07473309
## 6 45 146 30 48 -1.4576316 1.2402932 -0.44906814
## 9 6 50 5 6 -0.1254996 1.4185389 -0.44952287
## 10 48 132 12 24 -0.7107561 0.1351697 1.84223680
## 11 4 8 4 3 -0.5417908 -0.3995675 -0.49330720
## 13 6 55 20 122 0.3146672 -0.8273573 -0.65236796
## 14 8 11 18 31 0.7303463 -0.9343047 -0.83772977
## 16 1 17 7 14 -0.3011990 -0.9699538 0.14738356
## 17 2 16 11 14 3.0107652 -0.8630064 -0.25264595
## 19 5 22 7 22 0.5087324 -0.6847607 -0.54684114
## 20 72 371 44 123 -0.9293090 -0.2569709 1.61735105
## 23 143 346 138 217 0.2754869 1.1689949 -0.39530410
## 24 2 2 2 2 0.3011990 -1.8611825 1.51526431
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 13.57737 3.085766 17.897445 59.246715 0.000000 2.468613
## 2 62.35779 5.584280 29.472590 85.315391 13.650463 22.337121
## 3 51.09340 19.651307 7.860523 55.023660 11.790784 3.930261
## 5 60.06681 21.314029 23.251668 75.567922 15.501112 40.690420
## 6 64.86662 10.424992 52.124961 169.116540 34.749974 55.599958
## 9 58.82059 5.882059 17.646177 147.051471 14.705147 17.646177
## 10 26.51397 6.399924 21.942596 60.342140 5.485649 10.971298
## 11 17.79575 28.473196 14.236598 28.473196 14.236598 10.677449
## 13 134.04896 47.311398 5.256822 48.187535 17.522740 106.888715
## 14 111.69586 26.062367 14.892781 20.477574 33.508758 57.709527
## 16 63.64513 28.929605 1.928640 32.786885 13.500482 27.000964
## 17 97.38031 36.717166 3.192797 25.542376 17.560384 22.349579
## 19 143.48826 54.565958 10.104807 44.461151 14.146730 44.461151
## 20 110.54932 94.439119 19.998872 103.049745 12.221533 34.164740
## 23 86.67502 5.221387 49.777221 120.439989 48.036759 75.536062
## 24 13.21448 8.809654 2.936551 2.936551 2.936551 2.936551V2RMAuto
split <- sample.split(df_segvial$stV2RMAuto, SplitRatio = 2/3)
training_set_v2rma <- subset(df_segvial, split==T)
test_set_v2rma <- subset(df_segvial, split==F)
training_set_v2rma## Codigo.Pais Pais Ciudad Poblacion DenPob ArCiclista
## 1 ES Spain Barcelona 1620343 2.22857298 0.40404755
## 2 ES Spain Madrid 3223334 -0.03195466 -0.60455235
## 5 FR France Lyon 516092 1.11629688 -0.09417650
## 6 FR France Marseille 863310 -0.40609928 -0.88404389
## 8 FR France Nantes 309346 -0.16485218 2.55491722
## 9 FR France Nice 340017 -0.18982338 -0.73822222
## 10 FR France Paris 2187526 3.23546219 0.84151257
## 11 FR France Strasbourg 280966 -0.40038554 2.62782805
## 12 FR France Toulouse 479553 -0.30028915 1.14530772
## 14 GB United Kingdom Bradford 537173 -0.85029021 -1.05416917
## 16 GB United Kingdom Edinburgh 518500 -0.75844702 0.02734156
## 17 GB United Kingdom Glasgow 626410 -0.40842711 -0.60455235
## 19 GB United Kingdom Liverpool 494814 -0.37647245 -1.01771375
## 21 GB United Kingdom Manchester 547627 -0.15829196 -0.42227526
## 23 IT Italy Rome 2872800 -0.68776585 -1.09062459
## 24 NO Norway Oslo 681071 -0.86044799 0.56202103
## ArBajaVel PMPeatones PMCiclistas PMTPublico PMVMotor PeatAuto CicAuto
## 1 1.8984595 0.6615604 -0.27804757 1.6644700 -1.74922903 22 5
## 2 -0.9238882 0.8266066 -0.80613791 0.2431521 -0.82481711 201 18
## 5 -0.0533331 0.8266066 -0.27804757 -0.2758958 -0.45835126 31 11
## 6 -0.7132059 0.8266066 -0.62096337 -1.0520421 0.18708763 56 9
## 8 0.2249265 0.2489448 0.75069985 -0.5669506 0.04365677 13 7
## 9 -0.7768081 1.3217454 -0.62096337 -1.0520421 -0.24320496 20 2
## 10 0.7695204 1.8994073 0.06486824 1.0823603 -2.46638335 58 14
## 11 -0.4389214 0.7440835 1.77944727 -0.9550238 -0.31492039 5 8
## 12 0.3481558 -0.2461940 0.06486824 -1.2460786 1.11938825 12 2
## 14 -1.0868689 -1.5665640 -0.62096337 0.8883237 0.83252652 60 14
## 16 1.7792053 0.8266066 -0.27804757 -0.2758958 -0.45835126 33 15
## 17 -0.8046341 0.2489448 -0.62096337 1.0823603 -0.88864385 61 23
## 19 -0.2202889 -0.9889021 -0.27804757 0.4032323 0.68909566 71 27
## 21 0.3799569 -1.1539484 -0.27804757 -0.4699323 1.40624998 40 34
## 23 -1.0669932 -1.5995733 -0.62096337 0.6845853 1.01181510 249 15
## 24 -0.2401646 0.6615604 0.75069985 0.4032323 -1.03207471 9 6
## V2RMSM V2RMAuto AutoSM AutoAuto Precipitacion Temp PBI
## 1 29 96 0 4 -1.1539838 2.2028201 -0.61130324
## 2 95 275 44 72 -2.0361540 1.0620475 -0.47866210
## 5 12 39 8 21 0.4799593 0.1708188 -0.07473309
## 6 45 146 30 48 -1.4576316 1.2402932 -0.44906814
## 8 6 13 1 6 0.4040474 0.1708188 -0.45043750
## 9 6 50 5 6 -0.1254996 1.4185389 -0.44952287
## 10 48 132 12 24 -0.7107561 0.1351697 1.84223680
## 11 4 8 4 3 -0.5417908 -0.3995675 -0.49330720
## 12 5 29 5 12 -0.7070829 0.8125034 -0.24376255
## 14 8 11 18 31 0.7303463 -0.9343047 -0.83772977
## 16 1 17 7 14 -0.3011990 -0.9699538 0.14738356
## 17 2 16 11 14 3.0107652 -0.8630064 -0.25264595
## 19 5 22 7 22 0.5087324 -0.6847607 -0.54684114
## 21 4 31 5 28 0.4609813 -0.7204098 0.03247566
## 23 143 346 138 217 0.2754869 1.1689949 -0.39530410
## 24 2 2 2 2 0.3011990 -1.8611825 1.51526431
## stPeatAuto stCicAuto stV2RMSM stV2RMAuto stAutoSM stAutoAuto
## 1 13.57737 3.085766 17.897445 59.246715 0.000000 2.468613
## 2 62.35779 5.584280 29.472590 85.315391 13.650463 22.337121
## 5 60.06681 21.314029 23.251668 75.567922 15.501112 40.690420
## 6 64.86662 10.424992 52.124961 169.116540 34.749974 55.599958
## 8 42.02414 22.628384 19.395758 42.024141 3.232626 19.395758
## 9 58.82059 5.882059 17.646177 147.051471 14.705147 17.646177
## 10 26.51397 6.399924 21.942596 60.342140 5.485649 10.971298
## 11 17.79575 28.473196 14.236598 28.473196 14.236598 10.677449
## 12 25.02330 4.170550 10.426376 60.472982 10.426376 25.023303
## 14 111.69586 26.062367 14.892781 20.477574 33.508758 57.709527
## 16 63.64513 28.929605 1.928640 32.786885 13.500482 27.000964
## 17 97.38031 36.717166 3.192797 25.542376 17.560384 22.349579
## 19 143.48826 54.565958 10.104807 44.461151 14.146730 44.461151
## 21 73.04242 62.086055 7.304242 56.607874 9.130302 51.129692
## 23 86.67502 5.221387 49.777221 120.439989 48.036759 75.536062
## 24 13.21448 8.809654 2.936551 2.936551 2.936551 2.936551Dado que tenemos que realizar el mismo procedimiento para cada una de las variables de respuesta, se trabajara todo de a una pero siguiendo los mismos pasos que son:
Seleccionar el mejor modelo usando lasso
Obtener los coeficientes
Calcular el MSE
PeatAuto
# Matrices de entrenamiento y test
x_train_pa <- model.matrix(stPeatAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = training_set_pa)[, -1]
y_train_pa <- training_set_pa$stPeatAuto
x_test_pa <- model.matrix(stPeatAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = test_set_pa)[, -1]
y_test_pa <- test_set_pa$stPeatAuto# Creación y entrenamiento del modelo
# Para obtener un ajuste con regularización Lasso se indica argumento alpha=1.
# Si no se especifica valor de lambda, se selecciona un rango automático.
modelo_pa <- glmnet(
x = x_train_pa,
y = y_train_pa,
alpha = 1,
nlambda = 100,
standardize = TRUE
)# Evolución de los coeficientes en función de lambda
regularizacion_pa <- modelo_pa$beta %>%
as.matrix() %>%
t() %>%
as_tibble() %>%
mutate(lambda = modelo_pa$lambda)
regularizacion_pa <- regularizacion_pa %>%
pivot_longer(
cols = !lambda,
names_to = "predictor",
values_to = "coeficientes"
)
regularizacion_pa %>%
ggplot(aes(x = lambda, y = coeficientes, color = predictor)) +
geom_line() +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))
) +
labs(title = "Coeficientes del modelo en función de la regularización") +
theme_bw() +
theme(legend.position = "none")
Puede verse como, a medida que aumenta el valor de lambda, la regularización es mayor y más predictores quedan excluidos (su coeficiente es 0).
Para identificar el valor de lambda que da lugar al mejor modelo, se
puede recurrir a validación cruzada con la función
cv.glmnet().
# Evolución del error en función de lambda
set.seed(123)
cv_error_pa <- cv.glmnet(
x = x_train_pa,
y = y_train_pa,
alpha = 1,
nfolds = 10,
type.measure = "mse",
standardize = TRUE
)## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## foldplot(cv_error_pa)
# Mejor valor lambda encontrado
paste("Mejor valor de lambda encontrado:", cv_error_pa$lambda.min)## [1] "Mejor valor de lambda encontrado: 11.353822308282"# Mejor valor lambda encontrado + 1sd
# Mayor valor de lambda con el que el test-error no se aleja más de 1sd del mínimo.
paste("Mejor valor de lambda encontrado + 1 desviación estándar:", cv_error_pa$lambda.1se)## [1] "Mejor valor de lambda encontrado + 1 desviación estándar: 18.0784959001297"Se entrena de nuevo el modelo, esta vez empleando el mayor valor de lambda cuyo error está a menos de una desviación típica del mínimo encontrado en la validación cruzada.
# Mejor modelo lambda óptimo + 1sd
modelo_pa <- glmnet(
x = x_train_pa,
y = y_train_pa,
alpha = 1,
lambda = cv_error_pa$lambda.min,
standardize = TRUE
)# Coeficientes del modelo
df_coeficientes_pa <- coef(modelo_pa) %>%
as.matrix() %>%
as_tibble(rownames = "predictor") %>%
rename(coeficiente = s0)
df_coeficientes_pa %>%
filter(predictor != "(Intercept)") %>%
ggplot(aes(x = predictor, y = coeficiente)) +
geom_col() +
labs(title = "Coeficientes del modelo Lasso") +
theme_bw() +
theme(axis.text.x = element_text(size = 6, angle = 45))
df_coeficientes_pa %>%
filter(
predictor != "(Intercept)",
coeficiente != 0
) ## # A tibble: 2 × 2
## predictor coeficiente
## <chr> <dbl>
## 1 PMPeatones -8.51
## 2 ArCiclista -14.6De los 10 predictores disponibles, el modelo final solo incluye 2
# Predicciones de entrenamiento
predicciones_train_pa <- predict(modelo_pa, newx = x_train_pa)
# MSE de entrenamiento
training_mse_pa <- mean((predicciones_train_pa - y_train_pa)^2)
paste("Error (mse) de entrenamiento:", training_mse_pa)## [1] "Error (mse) de entrenamiento: 528.745189314674"# Predicciones de test
predicciones_test_pa <- predict(modelo_pa, newx = x_test_pa)
# MSE de test
test_mse_lasso_pa <- mean((predicciones_test_pa - y_test_pa)^2)
paste("Error (mse) de test:", test_mse_lasso_pa)## [1] "Error (mse) de test: 1003.63880115104"CicAuto
# Matrices de entrenamiento y test
x_train_ca <- model.matrix(stCicAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = training_set_ca)[, -1]
y_train_ca <- training_set_ca$stCicAuto
x_test_ca <- model.matrix(stCicAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = test_set_ca)[, -1]
y_test_ca <- test_set_ca$stCicAuto# Creación y entrenamiento del modelo
modelo_ca <- glmnet(
x = x_train_ca,
y = y_train_ca,
alpha = 1,
nlambda = 100,
standardize = TRUE
)# Evolución de los coeficientes en función de lambda
regularizacion_ca <- modelo_ca$beta %>%
as.matrix() %>%
t() %>%
as_tibble() %>%
mutate(lambda = modelo_ca$lambda)
regularizacion_ca <- regularizacion_ca %>%
pivot_longer(
cols = !lambda,
names_to = "predictor",
values_to = "coeficientes"
)
regularizacion_ca %>%
ggplot(aes(x = lambda, y = coeficientes, color = predictor)) +
geom_line() +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))
) +
labs(title = "Coeficientes del modelo en función de la regularización") +
theme_bw() +
theme(legend.position = "none")
# Evolución del error en función de lambda
cv_error_ca <- cv.glmnet(
x = x_train_ca,
y = y_train_ca,
alpha = 1,
nfolds = 10,
type.measure = "mse",
standardize = TRUE
)## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## foldplot(cv_error_ca)
# Mejor valor lambda encontrado
paste("Mejor valor de lambda encontrado:", cv_error_ca$lambda.min)## [1] "Mejor valor de lambda encontrado: 8.53568813301055"# Mejor valor lambda encontrado + 1sd
paste("Mejor valor de lambda encontrado + 1 desviación estándar:", cv_error_ca$lambda.1se)## [1] "Mejor valor de lambda encontrado + 1 desviación estándar: 16.370683860737"No utilizamos el mejor modelo lambda óptimo + 1sd , dado que si hacemos eso no tenemos ningun coeficiente y nos queda unicamente el intercept
# Mejor modelo lambda óptimo min
modelo_ca <- glmnet(
x = x_train_ca,
y = y_train_ca,
alpha = 1,
lambda = cv_error_ca$lambda.min,
standardize = TRUE
)# Coeficientes del modelo
df_coeficientes_ca <- coef(modelo_ca) %>%
as.matrix() %>%
as_tibble(rownames = "predictor") %>%
rename(coeficiente = s0)
df_coeficientes_ca %>%
filter(predictor != "(Intercept)") %>%
ggplot(aes(x = predictor, y = coeficiente)) +
geom_col() +
labs(title = "Coeficientes del modelo Lasso") +
theme_bw() +
theme(axis.text.x = element_text(size = 6, angle = 45))
df_coeficientes_ca %>%
filter(
predictor != "(Intercept)",
coeficiente != 0
) ## # A tibble: 1 × 2
## predictor coeficiente
## <chr> <dbl>
## 1 PMPeatones -7.93# Predicciones de entrenamiento
predicciones_train_ca <- predict(modelo_ca, newx = x_train_ca)
# MSE de entrenamiento
training_mse_ca <- mean((predicciones_train_ca - y_train_ca)^2)
paste("Error (mse) de entrenamiento:", training_mse_ca)## [1] "Error (mse) de entrenamiento: 528.783266570171"# Predicciones de test
predicciones_test_ca <- predict(modelo_ca, newx = x_test_ca)
# MSE de test
test_mse_lasso_ca <- mean((predicciones_test_ca - y_test_ca)^2)
paste("Error (mse) de test:", test_mse_lasso_ca)## [1] "Error (mse) de test: 463.143134611149"AutoSM
# Matrices de entrenamiento y test
x_train_as <- model.matrix(stAutoSM ~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = training_set_as)[, -1]
y_train_as <- training_set_as$stAutoSM
x_test_as <- model.matrix(stAutoSM~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = test_set_as)[, -1]
y_test_as <- test_set_as$stAutoSM# Creación y entrenamiento del modelo
modelo_as <- glmnet(
x = x_train_as,
y = y_train_as,
alpha = 1,
nlambda = 100,
standardize = TRUE
)# Evolución de los coeficientes en función de lambda
regularizacion_as <- modelo_as$beta %>%
as.matrix() %>%
t() %>%
as_tibble() %>%
mutate(lambda = modelo_as$lambda)
regularizacion_as <- regularizacion_as %>%
pivot_longer(
cols = !lambda,
names_to = "predictor",
values_to = "coeficientes"
)
regularizacion_as %>%
ggplot(aes(x = lambda, y = coeficientes, color = predictor)) +
geom_line() +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))
) +
labs(title = "Coeficientes del modelo en función de la regularización") +
theme_bw() +
theme(legend.position = "none")
# Evolución del error en función de lambda
cv_error_as <- cv.glmnet(
x = x_train_as,
y = y_train_as,
alpha = 1,
nfolds = 10,
type.measure = "mse",
standardize = TRUE
)## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## foldplot(cv_error_as)
# Mejor valor lambda encontrado
paste("Mejor valor de lambda encontrado:", cv_error_as$lambda.min)## [1] "Mejor valor de lambda encontrado: 0.925567531378252"# Mejor valor lambda encontrado + 1sd
paste("Mejor valor de lambda encontrado + 1 desviación estándar:", cv_error_as$lambda.1se)## [1] "Mejor valor de lambda encontrado + 1 desviación estándar: 4.93947553313584"# Mejor modelo lambda minimo
modelo_as <- glmnet(
x = x_train_as,
y = y_train_as,
alpha = 1,
lambda = cv_error_as$lambda.min,
standardize = TRUE
)# Coeficientes del modelo
df_coeficientes_as <- coef(modelo_as) %>%
as.matrix() %>%
as_tibble(rownames = "predictor") %>%
rename(coeficiente = s0)
df_coeficientes_as %>%
filter(predictor != "(Intercept)") %>%
ggplot(aes(x = predictor, y = coeficiente)) +
geom_col() +
labs(title = "Coeficientes del modelo Lasso") +
theme_bw() +
theme(axis.text.x = element_text(size = 6, angle = 45))
df_coeficientes_as %>%
filter(
predictor != "(Intercept)",
coeficiente != 0
) ## # A tibble: 6 × 2
## predictor coeficiente
## <chr> <dbl>
## 1 PBI 2.68
## 2 Temp -0.155
## 3 PMPeatones -0.745
## 4 ArBajaVel -3.18
## 5 ArCiclista -1.42
## 6 DenPob -2.09# Predicciones de entrenamiento
predicciones_train_as <- predict(modelo_as, newx = x_train_pa)
# MSE de entrenamiento
training_mse_as <- mean((predicciones_train_as - y_train_as)^2)
paste("Error (mse) de entrenamiento:", training_mse_as)## [1] "Error (mse) de entrenamiento: 116.908401559133"# Predicciones de test
predicciones_test_as <- predict(modelo_as, newx = x_test_as)
# MSE de test
test_mse_lasso_as <- mean((predicciones_test_as - y_test_as)^2)
paste("Error (mse) de test:", test_mse_lasso_as)## [1] "Error (mse) de test: 272.172650464871"AutoAuto
# Matrices de entrenamiento y test
x_train_aa <- model.matrix(stAutoAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = training_set_aa)[, -1]
y_train_aa <- training_set_aa$stAutoAuto
x_test_aa <- model.matrix(stAutoAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = test_set_aa)[, -1]
y_test_aa <- test_set_pa$stAutoAuto# Creación y entrenamiento del modelo
modelo_aa <- glmnet(
x = x_train_aa,
y = y_train_aa,
alpha = 1,
nlambda = 100,
standardize = TRUE
)# Evolución de los coeficientes en función de lambda
regularizacion_aa <- modelo_aa$beta %>%
as.matrix() %>%
t() %>%
as_tibble() %>%
mutate(lambda = modelo_aa$lambda)
regularizacion_aa <- regularizacion_aa %>%
pivot_longer(
cols = !lambda,
names_to = "predictor",
values_to = "coeficientes"
)
regularizacion_pa %>%
ggplot(aes(x = lambda, y = coeficientes, color = predictor)) +
geom_line() +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))
) +
labs(title = "Coeficientes del modelo en función de la regularización") +
theme_bw() +
theme(legend.position = "none")
# Evolución del error en función de lambda
cv_error_aa <- cv.glmnet(
x = x_train_aa,
y = y_train_aa,
alpha = 1,
nfolds = 10,
type.measure = "mse",
standardize = TRUE
)## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## foldplot(cv_error_pa)
# Mejor valor lambda encontrado
paste("Mejor valor de lambda encontrado:", cv_error_aa$lambda.min)## [1] "Mejor valor de lambda encontrado: 12.2103925497714"# Mejor valor lambda encontrado + 1sd
paste("Mejor valor de lambda encontrado + 1 desviación estándar:", cv_error_aa$lambda.1se)## [1] "Mejor valor de lambda encontrado + 1 desviación estándar: 30.9577957281277"# Mejor modelo lambda óptimo + 1sd
modelo_aa <- glmnet(
x = x_train_aa,
y = y_train_aa,
alpha = 1,
lambda = cv_error_aa$lambda.min,
standardize = TRUE
)# Coeficientes del modelo
df_coeficientes_aa <- coef(modelo_aa) %>%
as.matrix() %>%
as_tibble(rownames = "predictor") %>%
rename(coeficiente = s0)
df_coeficientes_aa %>%
filter(predictor != "(Intercept)") %>%
ggplot(aes(x = predictor, y = coeficiente)) +
geom_col() +
labs(title = "Coeficientes del modelo Lasso") +
theme_bw() +
theme(axis.text.x = element_text(size = 6, angle = 45))
df_coeficientes_aa %>%
filter(
predictor != "(Intercept)",
coeficiente != 0
) ## # A tibble: 3 × 2
## predictor coeficiente
## <chr> <dbl>
## 1 PMCiclistas -1.23
## 2 PMPeatones -18.0
## 3 ArCiclista -1.06# Predicciones de entrenamiento
predicciones_train_aa <- predict(modelo_aa, newx = x_train_aa)
# MSE de entrenamiento
training_mse_aa <- mean((predicciones_train_aa - y_train_aa)^2)
paste("Error (mse) de entrenamiento:", training_mse_aa)## [1] "Error (mse) de entrenamiento: 1422.62892415536"# Predicciones de test
predicciones_test_aa <- predict(modelo_aa, newx = x_test_aa)
# MSE de test
test_mse_lasso_aa <- mean((predicciones_test_aa - y_test_aa)^2)
paste("Error (mse) de test:", test_mse_lasso_aa)## [1] "Error (mse) de test: 581.313261960649"V2RMSM
# Matrices de entrenamiento y test
x_train_v2rms <- model.matrix(stV2RMSM~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = training_set_v2rms)[, -1]
y_train_v2rms <- training_set_v2rms$stV2RMSM
x_test_v2rms <- model.matrix(stV2RMSM~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = test_set_v2rms)[, -1]
y_test_v2rms <- test_set_v2rms$stV2RMSM# Creación y entrenamiento del modelo
modelo_v2rms <- glmnet(
x = x_train_v2rms,
y = y_train_v2rms,
alpha = 1,
nlambda = 100,
standardize = TRUE
)# Evolución de los coeficientes en función de lambda
regularizacion_v2rms <- modelo_v2rms$beta %>%
as.matrix() %>%
t() %>%
as_tibble() %>%
mutate(lambda = modelo_v2rms$lambda)
regularizacion_v2rms <- regularizacion_v2rms %>%
pivot_longer(
cols = !lambda,
names_to = "predictor",
values_to = "coeficientes"
)
regularizacion_v2rms %>%
ggplot(aes(x = lambda, y = coeficientes, color = predictor)) +
geom_line() +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))
) +
labs(title = "Coeficientes del modelo en función de la regularización") +
theme_bw() +
theme(legend.position = "none")
# Evolución del error en función de lambda
cv_error_v2rms <- cv.glmnet(
x = x_train_v2rms,
y = y_train_v2rms,
alpha = 1,
nfolds = 10,
type.measure = "mse",
standardize = TRUE
)## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## foldplot(cv_error_v2rms)
# Mejor valor lambda encontrado
paste("Mejor valor de lambda encontrado:", cv_error_v2rms$lambda.min)## [1] "Mejor valor de lambda encontrado: 3.69457710861108"# Mejor valor lambda encontrado + 1sd
paste("Mejor valor de lambda encontrado + 1 desviación estándar:", cv_error_v2rms$lambda.1se)## [1] "Mejor valor de lambda encontrado + 1 desviación estándar: 9.3670996214073"# Mejor modelo lambda minimo
modelo_v2rms <- glmnet(
x = x_train_v2rms,
y = y_train_v2rms,
alpha = 1,
lambda = cv_error_v2rms$lambda.min,
standardize = TRUE
)# Coeficientes del modelo
df_coeficientes_v2rms <- coef(modelo_v2rms) %>%
as.matrix() %>%
as_tibble(rownames = "predictor") %>%
rename(coeficiente = s0)
df_coeficientes_v2rms %>%
filter(predictor != "(Intercept)") %>%
ggplot(aes(x = predictor, y = coeficiente)) +
geom_col() +
labs(title = "Coeficientes del modelo Lasso") +
theme_bw() +
theme(axis.text.x = element_text(size = 6, angle = 45))
df_coeficientes_v2rms %>%
filter(
predictor != "(Intercept)",
coeficiente != 0
) ## # A tibble: 3 × 2
## predictor coeficiente
## <chr> <dbl>
## 1 Precipitacion -0.945
## 2 Temp 4.79
## 3 ArBajaVel -0.283# Predicciones de entrenamiento
predicciones_train_v2rms <- predict(modelo_v2rms, newx = x_train_v2rms)
# MSE de entrenamiento
training_mse_v2rms <- mean((predicciones_train_v2rms - y_train_v2rms)^2)
paste("Error (mse) de entrenamiento:", training_mse_v2rms)## [1] "Error (mse) de entrenamiento: 131.558222446041"# Predicciones de test
predicciones_test_v2rms <- predict(modelo_v2rms, newx = x_test_v2rms)
# MSE de test
test_mse_lasso_v2rms <- mean((predicciones_test_v2rms - y_test_v2rms)^2)
paste("Error (mse) de test:", test_mse_lasso_v2rms)## [1] "Error (mse) de test: 67.0569785191742"V2RMAuto
# Matrices de entrenamiento y test
x_train_v2rma <- model.matrix(stV2RMAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = training_set_v2rma)[, -1]
y_train_v2rma <- training_set_pa$stV2RMAuto
x_test_v2rma <- model.matrix(stPeatAuto~ Precipitacion+PBI+Temp+PMVMotor+PMTPublico+PMCiclistas+PMPeatones+ArBajaVel+ArCiclista+DenPob, data = test_set_v2rma)[, -1]
y_test_v2rma <- test_set_v2rma$stV2RMAuto# Creación y entrenamiento del modelo
modelo_v2rma <- glmnet(
x = x_train_v2rma,
y = y_train_v2rma,
alpha = 1,
nlambda = 100,
standardize = TRUE
)# Evolución de los coeficientes en función de lambda
regularizacion_v2rma <- modelo_v2rma$beta %>%
as.matrix() %>%
t() %>%
as_tibble() %>%
mutate(lambda = modelo_v2rma$lambda)
regularizacion_v2rma <- regularizacion_v2rma %>%
pivot_longer(
cols = !lambda,
names_to = "predictor",
values_to = "coeficientes"
)
regularizacion_v2rma %>%
ggplot(aes(x = lambda, y = coeficientes, color = predictor)) +
geom_line() +
scale_x_log10(
breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x))
) +
labs(title = "Coeficientes del modelo en función de la regularización") +
theme_bw() +
theme(legend.position = "none")
# Evolución del error en función de lambda
cv_error_v2rma <- cv.glmnet(
x = x_train_v2rma,
y = y_train_v2rma,
alpha = 1,
nfolds = 10,
type.measure = "mse",
standardize = TRUE
)## Warning: from glmnet C++ code (error code -95); Convergence for 95th lambda
## value not reached after maxit=100000 iterations; solutions for larger lambdas
## returned## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## foldplot(cv_error_v2rma)
# Mejor valor lambda encontrado
paste("Mejor valor de lambda encontrado:", cv_error_v2rma$lambda.min)## [1] "Mejor valor de lambda encontrado: 10.5720911630665"# Mejor valor lambda encontrado + 1sd
paste("Mejor valor de lambda encontrado + 1 desviación estándar:", cv_error_v2rma$lambda.1se)## [1] "Mejor valor de lambda encontrado + 1 desviación estándar: 20.2763221289822"# Mejor modelo lambda minimo
modelo_v2rma <- glmnet(
x = x_train_v2rma,
y = y_train_v2rma,
alpha = 1,
lambda = cv_error_v2rma$lambda.min,
standardize = TRUE
)# Coeficientes del modelo
df_coeficientes_v2rma <- coef(modelo_v2rma) %>%
as.matrix() %>%
as_tibble(rownames = "predictor") %>%
rename(coeficiente = s0)
df_coeficientes_v2rma %>%
filter(predictor != "(Intercept)") %>%
ggplot(aes(x = predictor, y = coeficiente)) +
geom_col() +
labs(title = "Coeficientes del modelo Lasso") +
theme_bw() +
theme(axis.text.x = element_text(size = 6, angle = 45))
df_coeficientes_v2rma %>%
filter(
predictor != "(Intercept)",
coeficiente != 0
) ## # A tibble: 1 × 2
## predictor coeficiente
## <chr> <dbl>
## 1 Temp 9.01# Predicciones de entrenamiento
predicciones_train_v2rma <- predict(modelo_v2rma, newx = x_train_v2rma)
# MSE de entrenamiento
training_mse_v2rma <- mean((predicciones_train_v2rma - y_train_v2rma)^2)
paste("Error (mse) de entrenamiento:", training_mse_v2rma)## [1] "Error (mse) de entrenamiento: 1478.42574970098"# Predicciones de test
predicciones_test_v2rma <- predict(modelo_v2rma, newx = x_test_v2rma)
# MSE de test
test_mse_lasso_v2rma <- mean((predicciones_test_v2rma - y_test_v2rma)^2)
paste("Error (mse) de test:", test_mse_lasso_v2rma)## [1] "Error (mse) de test: 370.838806523125"Comparacion
df_comparacion <- data.frame(
modelo = c("PeatAuto", "Cicauto", "AutoSM", "AutoAuto", "V2RMSM","V2RMAuto"),
mse = c(test_mse_lasso_pa, test_mse_lasso_ca, test_mse_lasso_as,test_mse_lasso_aa, test_mse_lasso_v2rms, test_mse_lasso_v2rma)
)
ggplot(data = df_comparacion, aes(x = modelo, y = mse)) +
geom_col(width = 0.5) +
geom_text(aes(label = round(mse, 2)), vjust = -0.1) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Modelos
\[ \hat{PeatAuto} = 73.81-8.8\hat{PMPeatones}-10.8\hat{ArCiclista}\] \[\hat{CicAuto} = 22.6\] \[ \begin{multline} \hat{AutoSM} = 17.85-1.55\hat{PMPeatones}-4.86\hat{ArBajaVel}\\ -0.53\hat{ArCiclista}-0.44\hat{DenPob} \end{multline} \] \[ \begin{multline} \hat{AutoAuto} = 30.09-16.5\hat{Precipitacion}-0.27\hat{PBI}\\ -5.8\hat{Temp}-0.48\hat{PMCiclista}+0.15\hat{PMPeatones}\\ -5.15\hat{ArCiclista} \end{multline}\] \[ \begin{multline} \hat{V2RMSM} = 15.06-4.04\hat{Precipitacion}+3.5\hat{Temp}\\ +14.27\hat{PMVMotor}-1.22\hat{PMCiclista}\\ -12.86\hat{PMPeatones}-4.06\hat{ArBajaVel} \end{multline} \] \[ \hat{V2RMAuto} = 67.95+22.47\hat{Temp}-7.4\hat{ArCiclista} \]
Para los peatones y ciclistas se puede ver que un incremento en el área de ambos contribuirá a tener un menor índice. Para los casos donde están involucrados vehículos, se observa que la temperatura y las precipitaciones van a tener influencia en algunos casos de incremento, como para los indicadores de V2R y la temperatura y en otros de reducción, tal y como sucede con el MDS de AutoAuto para las precipitaciones, cabe destacar que uno no puede controlar el aumento o disminución de estos factores, pero sí se puede incentivar ciertas medidas considerándolos. Continuando en la línea de vehículos, los incrementos vinculados a zonas o áreas en la gran mayoría de estas métricas colaboran en forma de disminución, por lo que sería recomendable considerar acciones vinculadas a esto.
Recomendaciones
El objetivo principal de este trabajo es poder identificar políticas o accionables que permitan alcanzar nivel de fatalidad y accidentes de tránsito violentos cero, en diversas urbes. Algunas medidas que se podrían evaluar son:
Incrementar las zonas de bicisenda y baja velocidad, considerando el flujo tanto vehicular como peatonal y de ciclistas. La sugerencia es hacer un análisis y un plan en pasos para aumentar el área de estas zonas, principalmente donde hay mayor uso por parte de ciclistas y peatones que están más vulnerables a la hora de un accidente vial.
Cuando llueve se podría declarar algunas zonas peligrosas como de baja velocidad, considerando que es más complejo frenar en superficies resbaladizas.
Incorporar alguna normativa vial para ceder el paso a peatones y bicicletas, que en caso de no cumplirse sea multado. Dado que es probable que se respete con mayor rigurosidad en aquellos países que no es una costumbre esto.
Incentivar el uso de bicicletas o caminar, cuando la temperatura es adecuada con alguna política de prioridad de paso y/o en caso de contar con bicicletas públicas o de alquiler que estas tengan alguna tarifa diferencial o gratis
Política de pull para los casos que esté lloviendo o haga mucho calor cosa de reducir el volumen de autos
Transformar ciertas zonas sólo de uso peatonal o bicicleta en cierto momento del día o un día específico a la semana, esto es una forma más económica de aumentar el área de estos agentes sin necesidad de una inversión tan grande como podrían serlo áreas específicas para estos.